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Question

BlackTom AI · Accepted Answer

To begin, the question asks to fill in the blanks based on the regression outputs shown in Model 1 and Model 2.
Option 1: 'The estimated coefficient of experience (exper) in Model 1 is not reported because Model 1 includes only 'educ' as an explanatory variable (see output: educ coefficient is 0.0521, no exper shown).'
- This statement makes sense given typical regression output: when a model is estimated with a single explanatory variable (educ), any other variable (exper) that is not included in that model will not have an estimated coefficient reported. Since Model 1 only includes educ, the exper coefficient would indeed be omitted. The reference to the educ coefficient being 0.0521 aligns with a plausible output line for the included variable. This explanation correctly identifies why exper is not shown in Model 1.
- A potential misconception to avoid: one might wonder if exper exists in the data at all or if it is dropped due to collinearity. The provided note about Model 1 having only educ as an explanatory variable clarifies that exper simply isn’t part of that model specification, so no estimate is produced for it in Model 1.
Option 2: 'The estimated coefficient of experience (exper), β2, in Model 2 is 0.0858. This means that (holding education and exper^2 constant) an additional year of potential labor market experience is associated with an increase in hourly wage of approximately $0.0858.'
- This interpretation follows standard multiple regression logic: with educ and exper (and likely exper^2) in Model 2, the coefficient on exper (β2) represents the partial effect of an additional year of experience on the dependent variable, holding other included variables constant. The numeric value of 0.0858 would be the estimated change in the wage unit used (e.g., dollars per hour) per one more year of experience, assuming other terms in the model are kept fixed. The clause 'holding education and exper^2 constant' correctly notes that the effect is conditional on those other terms being fixed, which is typical for a model that includes a squared term for experience.
- A common pitfall is misinterpreting the presence of exper^2: the marginal effect of an extra year of experience is not simply β2 if a squared term is included. The correct interpretation would generally involve both β2 and the derivative with respect to experience, which includes the +2*β3*exper term if a quadratic is present. Since the given statement specifies 'holding education and exper^2 constant,' it effectively communicates the conditional interpretation, albeit in plain terms. If the model actually includes an exper^2 term, a more precise reading would acknowledge that the instantaneous effect depends on the current level of experience, not just a constant 0.0858, unless the context in the output treats β2 as the marginal effect at a specific reference level.
- A potential misconception to avoid: assuming the coefficient implies a constant $0.0858 increase for every additional year across all levels of experience without accounting for the squared term. The presence of exper^2 typically means the effect grows or diminishes with more experience, so the exact marginal impact should be evaluated at a particular experience level.
Overall, both statements align with standard interpretation of regression outputs: the first explains why Model 1 omits exper, and the second provides the stated coefficient and a conditional interpretation, with a note about how a squared term could modify the marginal effect across levels of experience.

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类似问题

Based on Question 13, now, add stotal to equation (4.17) on p.136 log(wage)=β0+β1jc+β2univ+β3exper+u and estimate the resulting equation by OLS. Is the coefficient on stotal statistically significant at the 5% significance level?

Based on Question 13, now, add stotal to equation (4.17) on p.136 log(wage)=β0+β1jc+β2univ+β3exper+u and estimate the resulting equation by OLS. Is the coefficient on stotal statistically significant at the 5% significance level?

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